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高山冰川遥感提取方法研究 总被引:1,自引:0,他引:1
刘梅 《测绘与空间地理信息》2017,(10):135-138,143
遥感的应用使得对冰川大尺度全覆盖、多时相变化的监测成为可能,然而冰川信息遥感提取方法的误差大等难题成为影响冰川监测的障碍。本文综合分析比较了目前已有的多种冰川提取方法的有效性,得出提取冰川范围精度最高的是面向对象的目视判读方法,其次是最大似然法监督分类、面向对象的自动分类、比值阈值法、雪盖指数法等。各自动方法提取冰川面积均有较大误差,且误差主要出现在冰舌末端、阴影区、薄冰区和云层遮盖范围等区域。本文将面向对象的目视判读法应用于冰川提取中,在保证信息提取精度的同时提高了传统解译的效率。 相似文献
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《测绘与空间地理信息》2020,(6)
为获取大范围最佳冰川边界,本文以西昆龙冰川为研究对象,利用融合后的Landsat-8OLI数据,依据纹理和亮度等规则,使用面向对象-NDSI方法结合大津算法提取了多期单时相2015年冰川范围信息。最后,使用多时相冰川范围信息,进行迭代综合分析,得到了最佳的西昆龙冰川边界。该信息提取总体分类精度高达99%以上,证明面向对象-NDSI方法结合大津算法能够实现高海拔冰川全方位的信息提取。本实验将图像分割大津算法应用于冰川阈值信息获取,实现了客观、快速和准确的影像分割过程。 相似文献
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高光谱遥感图像的监督分类 总被引:1,自引:0,他引:1
图像分类是高光谱遥感图像分析与应用的重要手段。总结了目前用于高光谱图像监督分类的主要方法,包括最小距离法、最大似然法、神经元网络法和支持向量机法,分析了上述方法的特点,并探讨了高光谱遥感图像分类方法的发展趋势。 相似文献
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分层分区分景相结合的区域土地利用/覆盖分类方法——以浙江钱塘江流域分类为例 总被引:2,自引:0,他引:2
在土地利用/覆盖研究中,对于范围广、地域差别大的地区,仅用同一标准对遥感图像进行分类往往难以得到理想的效果。
本文以浙江省钱塘江流域为例,通过对Landsat TM数据各波段组合,首先提取水层和山层信息,然后采用掩模法提取平原丘陵层信
息,并根据地形地貌和土地利用现状的差异,将平原丘陵层划分为6个区,当所划分区域内各景的影像时相不一致时,再对该区进
行分景处理。最后,分别对每层、每区和每景图像进行训练样本的选择和监督分类。试验结果表明,结合分层、分区和分景的监督
分类方法是一种适合于较大区域土地利用/土地覆盖分类的有效方法。 相似文献
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Timothy G. Whiteside Guy S. Boggs Stefan W. Maier 《International Journal of Applied Earth Observation and Geoinformation》2011
The development of robust object-based classification methods suitable for medium to high resolution satellite imagery provides a valid alternative to ‘traditional’ pixel-based methods. This paper compares the results of an object-based classification to a supervised per-pixel classification for mapping land cover in the tropical north of the Northern Territory of Australia. The object-based approach involved segmentation of image data into objects at multiple scale levels. Objects were assigned classes using training objects and the Nearest Neighbour supervised and fuzzy classification algorithm. The supervised pixel-based classification involved the selection of training areas and a classification using the maximum likelihood classifier algorithm. Site-specific accuracy assessment using confusion matrices of both classifications were undertaken based on 256 reference sites. A comparison of the results shows a statistically significant higher overall accuracy of the object-based classification over the pixel-based classification. The incorporation of a digital elevation model (DEM) layer and associated class rules into the object-based classification produced slightly higher accuracies overall and for certain classes; however this was not statistically significant over the object-based using spectral information solely. The results indicate object-based analysis has good potential for extracting land cover information from satellite imagery captured over spatially heterogeneous land covers of tropical Australia. 相似文献
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面向土地利用分类的HJ-1 CCD影像最佳分形波段选择 总被引:2,自引:0,他引:2
环境一号卫星(HJ-1)CCD影像光谱波段较少,地物之间的准确分类识别有一定困难。采用分形纹理辅助地物分类识别是一种有效方法,而波段选择是提高分类识别精度的关键。本文以江西赣州定南县土地利用分类为例,采用双毯覆盖模型对HJ卫星CCD影像6类典型地物的波谱分形特征进行了分析,利用不同地物在不同波段上的分形区分度差异构建了最佳分形波段选择模型,并利用该模型挑选出最佳分形波段来辅助土地利用分类,最后对分类结果进行检验。结果表明:最佳分形波段选择模型能够综合权衡不同地物在不同波段上的分形区分度差异,利用挑选出来的最佳分形波段来辅助分类,其分类总体精度相对于原始影像分类提高了11.77%,相对于第1主成分分形辅助下的分类提高了1.56%。 相似文献
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广义马尔可夫随机场及其在多光谱纹理影像分类中的应用 总被引:1,自引:0,他引:1
在二维马尔可夫随机场模型的基础上,提出顾及波段间的空间相关性,发展了一种适用于多光谱纹理影像分类的广义马尔可夫随机场模型。鉴于广义马尔可夫随机场模型的复杂性,利用最大伪似然法建立了求解模型参数的简化方程式,实现了纹理特征的快速提取。结合提取的纹理特征影像和光谱特征影像,采用概率松弛算法实现影像的分类。实验证明,提出的基于广义马尔可夫随机场的多光谱纹理影像分类算法克服了传统的基于光谱特征的分类算法的局限性,提高了纹理影像的分类精度。 相似文献
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AbstractLand use/land cover (LULC) classification with high accuracy is necessary, especially in eco-environment research, urban planning, vegetation condition study and soil management. Over the last decade a number of classification algorithms have been developed for the analysis of remotely sensed data. The most notable algorithms are the object-oriented K-Nearest Neighbour (K-NN), Support Vector Machines (SVMs) and the Decision Trees (DTs) amongst many others. In this study, LULC types of Selangor area were analyzed on the basis of the classification results acquired using the pixel-based and object-based image analysis approaches. SPOT 5 satellite images with four spectral bands from 2003 and 2010 were used to carry out the image classification and ground truth data were collected from Google Earth and field trips. In pixel-based image analysis, a supervised classification was performed using the DT classifier. On the other hand, object-oriented (K-NN) image analysis was evaluated using standard nearest neighbour as classifier. Subsequently SVM object-based classification was performed. Five LULC categories were extracted and the results were compared between them. The overall classification accuracies for 2003 and 2010 showed that the object-oriented (K-NN) (90.5% and 91%) performed better results than the pixel-based DT (68.6% and 68.4%) and object-based SVM (80.6% and 78.15%). In general, the object-oriented (K-NN) performed better than both DTs and SVMs. The obtained LULC classification maps can be used to improve various applications such as change detection, urban design, environmental management and zooning. 相似文献
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Shivesh Kishore Karan 《国际地球制图》2018,33(10):1084-1094
The main objective of this study was to improve the long-term land use change detection by improving classification accuracy of previous generation satellite image using a recent super-resolution technique. The study also analysed the change in land cover over a period of 41 years in a coal mining area. A dual-tree complex wavelet transform-based image super-resolution technique was used to enhance Landsat images of 1975 and 2016. Separating pixels with similar spectral response is an enigmatical task, especially when those pixel represent different ground features. Therefore, an advanced neural net supervised classifier was used to minimize classification errors. Accuracy of the classified images (both super-resolved and original) were measured using confusion matrices and kappa coefficients. A significant improvement of more than 10% was observed in the overall classification accuracy for the image of 1975, highlighting that the classification accuracy of earlier generation satellite data can be improved substantially. 相似文献
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本论文尝试讨论两个主题:主题一为利用主成分分析PCA方法应用于像元阶层资料融合技术的研究。主题二为应用Dempster-Shafer evidence theory方法于特征阶层数据融合技术的研究。在第一个主题中,由于合成孔径雷达的数据具有全偏极特性,在此选取了对植被较为敏感的HV极化合成孔径雷达数据,与具有光谱特性的光学SPOT数据做数据融合处理以利接下来的地物分类。首先,本研究利用小波转换技术来滤除合成孔径雷达斑驳噪声,在接下来融合步骤中,主成分分析出来的第一部分(PCI)是用做完滤除噪声后的合成孔径雷达取代,在数据融合后,进行地物分类是采用最大似然法来分类融合影像。在第二个主题中,利用全偏极雷达数据的极化特性结合SPOT数据的光谱特性,其主要目的是为了增加分类的精确度。首先使用李式滤波器滤除全偏极雷达数据噪声,接下来同样是使用采用最大似然法来分类融合影像,(不同的在于全偏极雷达影像使用Wishart几率分布,在光学影像采用multivariate Gaussian几率分布)将每个类别中每个像元属于某个类别的几率值计算出来,再利用Dempster-Shafer evidence theory来结合这些类别的机率值。最后产生出一张新的分类影像。实验的结果显示分类的精确度比较于未融合的资料都有明显提升的效果,也证明了此两个数据融合方法对于不同数据特性的融合都是很成功的。 相似文献